Egészségtudományok Doktori Iskola

Állandó link (URI) ehhez a gyűjteményhez

https://hdl.handle.net/2437/2313

Általános Orvostudományi Kar

Egészségtudományok Doktori Iskola
(vezető: Dr. Harangi Mariann)

Orvostudományi doktori tanács

D46

Doktori programok:

  • Megelőző orvostan és népegészségtan
    (programvezető: Dr. Ádány Róza)
  • Anyagcsere és endokrin betegségek megelőzése és kontrollja
    (programvezető: Dr. Paragh György)

Böngészés

Egészségtudományok Doktori Iskola Szerző szerinti böngészés "Általános Orvostudományi Kar::Népegészség-és Járványtani Intézet"

Megjelenítve 1 - 2 (Összesen 2)
  • Bélyegkép
    TételSzabadon hozzáférhető
    Examining the Impact of Roma Ethnicity and Socioeconomic Segregation on Healthcare Access and Expenditure in Hungary
    (2024-11-27) Kasabji, Feras; Sándor, János; Egészségtudományok doktori iskola; Általános Orvostudományi Kar::Népegészség-és Járványtani Intézet
    This thesis explores healthcare disparities in Hungary, focusing on the socio-economic status (SES), geographical, and structural determinants of General Medical Practitioners (GMP). With Roma ethnicity as a potential influencing factor. Using data from 4818 GMPs, the first study found that SES factors like education and employment, rather than ethnicity, were the primary drivers of reimbursement variation. A second analysis of 4359 GMPs revealed higher healthcare utilization rates in segregated areas (SAs) but significantly lower outpatient, imaging, and medication reimbursement compared to complementary areas (CAs). Despite increased hospitalization costs in SAs, overall insurance spending remained lower. These findings underscore the importance of addressing SES-based inequities through targeted policies and systemic reforms to reduce healthcare inequalities and improve outcomes for marginalized populations.
  • Bélyegkép
    TételSzabadon hozzáférhető
    Integrating Genetic and Conventional Risk Fcators for Improving Coronary Heart Disease Risk Prediction
    (2023) Nasr, Nayla; Fiatal, Szilvia; Egészségtudományok Doktori Iskola; Egészségtudományi Kar; Általános Orvostudományi Kar::Népegészség-és Járványtani Intézet
    Introduction: Coronary heart disease (CHD) is a global health concern, and preventive intervention is available for high-risk individuals. Objectives: The study aims to summarize genetic and conventional risk factor (CRF) modelling studies for CHD risk prediction, assess the performance of risk prediction models, and evaluate the potential improvement by incorporating genetic information. Our study also aims to compare the sociodemographic, and lifestyle factors associated with CHD risk in the Hungarian (Roma and general) populations, estimate the allele frequencies of genetic risk scores (GRSs), and develop new models (by integrating GRS and CRFs) for predicting CHD/AMI risk among the Hungarian populations and assess their performance. Methods: A systematic review and cross-sectional were conducted, utilizing various databases (Embase, PubMed, Cochrane, Web of Science, and Scopus), and multivariable regression analyses (ROC curve analyses were also performed to assess the models' performances). Genetic analyses involved the calculation of GRSs and weighted GRS weres from the genotype data of 558 participants Results: The review identified coronary artery calsification (CAC) as an effective predictor of CHD risk. GRS analysis revealed differences between the general and Roma populations, suggesting higher genetic predisposition among the general population, and greater susceptibility to environmental factors among Roma. Socioeconomic disparities were noted, influencing health outcomes. Roma had a higher prevalence rate of various diseases but differed in certain risk factors like smoking and lipid levels. integration of CRFs and GRSs improved CHD prediction, with age, medication of elevated total cholesterol, and hypertension emerging as significant predictors. Conclusion: Age and cholesterol medication were key predictors for CHD among the general population while hypertension medication was predominant among among the Roma. population. Integrating CRFs and genetic components enhanced predictive performance, emphasizing the importance of comprehensive risk assessment for CHD prevention.